🤖 AI Summary
This work addresses the challenges of extreme class imbalance, dynamically evolving fraud patterns, and complex transactional relationships in credit card fraud detection by proposing a temporal-aware multi-relational graph neural network. The method constructs a dynamic multi-relational graph and employs a temporal relational attention mechanism to adaptively model both semantic and temporal dependencies among transactions. To enhance discrimination of rare fraud patterns, contrastive learning is integrated into the decoder. The model is jointly optimized using InfoNCE loss and Focal Loss, effectively mitigating the class imbalance issue. Experimental results demonstrate that the proposed approach significantly improves fraud detection accuracy, reduces false negatives, and exhibits superior generalization performance under highly imbalanced conditions.
📝 Abstract
In recent years, credit card fraud detection has faced significant challenges due to highly imbalanced data, evolving fraud patterns, and complex relational structures among transaction entities. To address these issues, this research proposes a novel framework called Timeaware Multi Relational Guided Graph Neural Network (TMR GGNN). Particularly, the proposed TMR GGNN extends the encoder decoder Graph Neural Network GNN architecture by modeling heterogeneous interactions across customers, merchants, devices, and IPs over temporal windows. Subsequently, the proposed TMR GGNN approach constructs a dynamic, multi relational graph and incorporates a time aware relational attention mechanism within the encoder to adaptively weigh the transaction relevance based on temporal proximity and semantic context. Consequently, the decoder employs a contrastive learning module to distinguish between real and synthesized transaction patterns, while improving the models generalization of rare fraud cases. Additionally, to effectively manage severe class imbalances and emphasize discriminative learning, a composite loss function combining Information Noise Contrastive Estimation (InfoNCE) based contrastive loss with Focal Loss is introduced. This integration assists in improving fraud identification while mitigating false negatives.